Abstract
The development of unmanned aerial manipulators (UAMs) allows a novel class of flying robots to carry out a wide variety of tasks in difficult environments due to their versatility and autonomy. However, the different tasks that can be carried out might call for different control strategies. To this end, one needs to categorize the possible tasks accomplishable by UAMs. This paper proposes a novel taxonomy, which is the result of a video information acquisition methodology combined with a review of research works in the literature. The different elements of the taxonomy are separated using a higher level of abstraction in a way that the general description of the tasks are considered and not its operational details. To illustrate the fact that algorithms must adapt to different tasks, a description of the usual UAM architecture is carried out. Four categories of criteria are used in the taxonomy to differentiate all possible tasks. These categories are the interaction type, the actual task definition, the environment condition and the time sensitivity of the task. This taxonomy forms the basis for possible machine-learning-based task classifiers that could be used in autonomous UAMs control and mission planning. Multiple tasks defined in the taxonomy can be combined to accomplish complex missions.
